PO.DAAC ECCO SSH

Reading ECCO Sea Surface Height (SSH) Data Using Kerchunk Reference File

Many of NASA’s current and legacy data collections are archive in netCDF4 format. By itself, netCDF4 are not cloud optimized and reading these files can take as long from a personal/local work environment as it takes to read the data from a working environment deployed in the cloud. Using Kerchunk, we can treat these files as cloud optimized assets by creating metadata json file describing existing netCDF4 files, their chunks, and where to access them. The json reference files can be read in using Zarr and Xarray for efficient reads and fast processing.

Requirements

1. AWS instance running in us-west-2

NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.

2. Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.

3. netrc File

You will need a netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.

Import required packages

import requests
import xarray as xr
import ujson
import s3fs
import fsspec
from tqdm import tqdm
from glob import glob
import os
import pathlib
import hvplot.xarray

from kerchunk.hdf import SingleHdf5ToZarr
from kerchunk.combine import MultiZarrToZarr

# The xarray produced from the reference file throws a SerializationWarning for each variable. Will need to explore why
import warnings
warnings.simplefilter("ignore")

Create Dask client to process the output json file in parallel

Generating the Kerchunk reference file can take some time depending on the internal structure of the data. Dask allows us to execute the reference file generation process in parallel, thus speeding up the overall process.

import dask
from dask.distributed import Client
client = Client(n_workers=4)
client

2022-05-11 15:27:29,674 - distributed.diskutils - INFO - Found stale lock file and directory '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-mezhdsy7', purging
/srv/conda/envs/notebook/lib/python3.9/contextlib.py:126: UserWarning: Creating scratch directories is taking a surprisingly long time. This is often due to running workers on a network file system. Consider specifying a local-directory to point workers to write scratch data to a local disk.
  next(self.gen)

Client

Client-ddf55e52-d13e-11ec-818c-b6609e8b92a4

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:41805/status

Cluster Info

LocalCluster

a24e60d3

Dashboard: http://127.0.0.1:41805/status	Workers: 4
Total threads: 4	Total memory: 15.18 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-8e045442-a409-4c3b-8c8c-a95470883931

Comm: tcp://127.0.0.1:36901	Workers: 4
Dashboard: http://127.0.0.1:41805/status	Total threads: 4
Started: Just now	Total memory: 15.18 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:34235	Total threads: 1
Dashboard: http://127.0.0.1:42845/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:37927
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-869qv5xb

Worker: 1

Comm: tcp://127.0.0.1:40997	Total threads: 1
Dashboard: http://127.0.0.1:41189/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:35257
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-3mo0d80c

Worker: 2

Comm: tcp://127.0.0.1:46429	Total threads: 1
Dashboard: http://127.0.0.1:42211/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:34287
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-o2fvmao4

Worker: 3

Comm: tcp://127.0.0.1:41615	Total threads: 1
Dashboard: http://127.0.0.1:41507/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:43053
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-9u77hywd

Get temporary S3 credentials

Temporary S3 credentials need to be passed to AWS. Note, these credentials must be refreshed after 1 hour.

s3_cred_endpoint = {
    'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',
    'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',
    'ornldaac':'https://data.ornldaac.earthdata.nasa.gov/s3credentials',
    'gesdisc':'https://data.gesdisc.earthdata.nasa.gov/s3credentials'
}

def get_temp_creds():
    temp_creds_url = s3_cred_endpoint['podaac']
    return requests.get(temp_creds_url).json()

temp_creds_req = get_temp_creds()

Direct Access a single netCDF4 file

Pass temporary credentials to our filesystem object to access the S3 assets

fs = s3fs.S3FileSystem(
    anon=False,
    key=temp_creds_req['accessKeyId'],
    secret=temp_creds_req['secretAccessKey'],
    token=temp_creds_req['sessionToken']
)

url = 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_latlon_0p50deg.nc'

s3_file_obj = fs.open(url, mode='rb')

Time how long it takes to directly access a cloud asset for comparisons later.

%%time

xr_ds = xr.open_dataset(s3_file_obj, chunks='auto', engine='h5netcdf')
xr_ds

CPU times: user 228 ms, sys: 8.51 ms, total: 237 ms
Wall time: 272 ms

<xarray.Dataset>
Dimensions:         (time: 1, latitude: 360, longitude: 720, nv: 2)
Coordinates:
  * time            (time) datetime64[ns] 2015-01-16T12:00:00
  * latitude        (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75
  * longitude       (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8
    time_bnds       (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
    latitude_bnds   (latitude, nv) float32 dask.array<chunksize=(360, 2), meta=np.ndarray>
    longitude_bnds  (longitude, nv) float32 dask.array<chunksize=(720, 2), meta=np.ndarray>
Dimensions without coordinates: nv
Data variables:
    SSH             (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHIBC          (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHNOIBC        (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
Attributes: (12/57)
    acknowledgement:              This research was carried out by the Jet Pr...
    author:                       Ian Fenty and Ou Wang
    cdm_data_type:                Grid
    comment:                      Fields provided on a regular lat-lon grid. ...
    Conventions:                  CF-1.8, ACDD-1.3
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            2015-02-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          2015-01-01T00:00:00
    title:                        ECCO Sea Surface Height - Monthly Mean 0.5 ...
    uuid:                         088d03b8-4158-11eb-876b-0cc47a3f47f1

xarray.Dataset

• Dimensions:
  • time: 1
  • latitude: 360
  • longitude: 720
  • nv: 2
• Coordinates: (6)
  • time
    (time)
    datetime64[ns]
    2015-01-16T12:00:00

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    long_name :

    center time of averaging period

    standard_name :

    time

    array(['2015-01-16T12:00:00.000000000'], dtype='datetime64[ns]')

  • latitude
    (latitude)
    float32
    -89.75 -89.25 ... 89.25 89.75

    axis :

    Y

    bounds :

    latitude_bnds

    comment :

    uniform grid spacing from -89.75 to 89.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    latitude at grid cell center

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)

  • longitude
    (longitude)
    float32
    -179.8 -179.2 ... 179.2 179.8

    axis :

    X

    bounds :

    longitude_bnds

    comment :

    uniform grid spacing from -179.75 to 179.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    longitude at grid cell center

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
          dtype=float32)

  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    comment :

    Start and end times of averaging period.

    coverage_content_type :

    coordinate

    long_name :

    time bounds of averaging period

    Array Chunk Bytes 16 B 16 B Shape (1, 2) (1, 2) Count 2 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray

  • latitude_bnds
    (latitude, nv)
    float32
    dask.array<chunksize=(360, 2), meta=np.ndarray>

    coverage_content_type :

    coordinate

    long_name :

    latitude bounds grid cells

    Array Chunk Bytes 2.81 kiB 2.81 kiB Shape (360, 2) (360, 2) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • longitude_bnds
    (longitude, nv)
    float32
    dask.array<chunksize=(720, 2), meta=np.ndarray>

    coverage_content_type :

    coordinate

    long_name :

    longitude bounds grid cells

    Array Chunk Bytes 5.62 kiB 5.62 kiB Shape (720, 2) (720, 2) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (3)
  • SSH
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    coverage_content_type :

    modelResult

    long_name :

    Dynamic sea surface height anomaly

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    comment :

    Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    valid_min :

    -1.8805772066116333

    valid_max :

    1.4207719564437866

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (1, 360, 720) (1, 360, 720) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • SSHIBC
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    coverage_content_type :

    modelResult

    long_name :

    The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading

    units :

    m

    comment :

    Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    valid_min :

    -0.30144819617271423

    valid_max :

    0.5245633721351624

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (1, 360, 720) (1, 360, 720) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • SSHNOIBC
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    coverage_content_type :

    modelResult

    long_name :

    Sea surface height anomaly without the inverted barometer (IB) correction

    units :

    m

    comment :

    Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.

    valid_min :

    -1.6654272079467773

    valid_max :

    1.4550364017486572

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (1, 360, 720) (1, 360, 720) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

• Attributes: (57)

  acknowledgement :

  This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.

  author :

  Ian Fenty and Ou Wang

  cdm_data_type :

  Grid

  comment :

  Fields provided on a regular lat-lon grid. They have been mapped to the regular lat-lon grid from the original ECCO lat-lon-cap 90 (llc90) native model grid. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations.

  Conventions :

  CF-1.8, ACDD-1.3

  coordinates_comment :

  Note: the global 'coordinates' attribute describes auxillary coordinates.

  creator_email :

  ecco-group@mit.edu

  creator_institution :

  NASA Jet Propulsion Laboratory (JPL)

  creator_name :

  ECCO Consortium

  creator_type :

  group

  creator_url :

  https://ecco-group.org

  date_created :

  2020-12-18T09:39:51

  date_issued :

  2020-12-18T09:39:51

  date_metadata_modified :

  2021-03-15T22:07:49

  date_modified :

  2021-03-15T22:07:49

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.5

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  0.5

  geospatial_lon_units :

  degrees_east

  history :

  Inaugural release of an ECCO Central Estimate solution to PO.DAAC

  id :

  10.5067/ECG5M-SSH44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE > OCEANS > SEA SURFACE TOPOGRAPHY > SEA SURFACE HEIGHT, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Public Domain

  metadata_link :

  https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_05DEG_MONTHLY_V4R4

  naming_authority :

  gov.nasa.jpl

  platform :

  ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)

  platform_vocabulary :

  GCMD platform keywords

  processing_level :

  L4

  product_name :

  SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_latlon_0p50deg.nc

  product_time_coverage_end :

  2018-01-01T00:00:00

  product_time_coverage_start :

  1992-01-01T12:00:00

  product_version :

  Version 4, Release 4

  program :

  NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)

  project :

  Estimating the Circulation and Climate of the Ocean (ECCO)

  publisher_email :

  podaac@podaac.jpl.nasa.gov

  publisher_institution :

  PO.DAAC

  publisher_name :

  Physical Oceanography Distributed Active Archive Center (PO.DAAC)

  publisher_type :

  institution

  publisher_url :

  https://podaac.jpl.nasa.gov

  references :

  ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928

  source :

  The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  summary :

  This dataset provides monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

  time_coverage_duration :

  P1M

  time_coverage_end :

  2015-02-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  2015-01-01T00:00:00

  title :

  ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4)

  uuid :

  088d03b8-4158-11eb-876b-0cc47a3f47f1

Specify a list of S3 URLs

Data Collection: ECCO_L4_SSH_05DEG_MONTHLY_V4R4
Time Range: 2015

urls = ['s3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2014-12_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-02_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-03_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-04_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-05_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-06_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-07_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-08_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-10_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-11_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-12_ECCO_V4r4_latlon_0p50deg.nc']

Generate the Kerchunk reference files.

Define a function to generate the Kerchunk reference files. These files can take a little time to generate.

def gen_json(u):
    so = dict(
        mode= "rb", 
        anon= False, 
        default_fill_cache= False,
        default_cache_type= "none"
    )
    with fs.open(u, **so) as infile:
        h5chunks = SingleHdf5ToZarr(infile, u, inline_threshold=300)
        with open(f"jsons/{u.split('/')[-1]}.json", 'wb') as outf:
            outf.write(ujson.dumps(h5chunks.translate()).encode())

Create output jsons directory if one does not exist.

pathlib.Path('./jsons/').mkdir(exist_ok=True)

Use the Dask Delayed function to create the Kerchunk reference file for each URL from the list of URLs in parallel

%%time

reference_files = []
for url in urls:
    ref = dask.delayed(gen_json)(url)
    reference_files.append(ref)

reference_files_compute = dask.compute(*reference_files)

CPU times: user 195 ms, sys: 83.4 ms, total: 278 ms
Wall time: 1.38 s

fs_ref_list = fsspec.filesystem('file')

reference_list = sorted([x for x in fs_ref_list.ls('jsons') if '.json' in x])
reference_list

['/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2014-12_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-02_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-03_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-04_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-05_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-06_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-07_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-08_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-10_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-11_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-12_ECCO_V4r4_latlon_0p50deg.nc.json']

Read single netCDF4 using Kerchunk reference file

Open the first reference file to read into an xarray dataset

with open(reference_list[0]) as j:
    reference = ujson.load(j)

Set configurations options

s_opts = {'skip_instance_cache':True}   #json
r_opts = {'anon':False,          
          'key':temp_creds_req['accessKeyId'], 
          'secret':temp_creds_req['secretAccessKey'], 
          'token':temp_creds_req['sessionToken']}    #ncfiles

fs_single = fsspec.filesystem("reference",
                              fo=reference,
                              ref_storage_args=s_opts,
                              remote_protocol='s3', 
                              remote_options=r_opts)

Read in a single reference object. We get a lot of SerializationWarnings which are ignored here using the warning package.
NOTE, the fill value, data range, min value, and max value may not match the source file. Will need to look into this more.

%%time

m = fs_single.get_mapper("")
ds_single = xr.open_dataset(m, engine="zarr", backend_kwargs={'consolidated':False}, chunks={})
ds_single

CPU times: user 56.3 ms, sys: 26 ms, total: 82.2 ms
Wall time: 221 ms

<xarray.Dataset>
Dimensions:         (time: 1, latitude: 360, longitude: 720, nv: 2)
Coordinates:
  * latitude        (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75
    latitude_bnds   (latitude, nv) float32 dask.array<chunksize=(360, 2), meta=np.ndarray>
  * longitude       (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8
    longitude_bnds  (longitude, nv) float32 dask.array<chunksize=(720, 2), meta=np.ndarray>
  * time            (time) datetime64[ns] 2014-12-16T12:00:00
    time_bnds       (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
Dimensions without coordinates: nv
Data variables:
    SSH             (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHIBC          (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHNOIBC        (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
Attributes: (12/57)
    Conventions:                  CF-1.8, ACDD-1.3
    acknowledgement:              This research was carried out by the Jet Pr...
    author:                       Ian Fenty and Ou Wang
    cdm_data_type:                Grid
    comment:                      Fields provided on a regular lat-lon grid. ...
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            2015-01-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          2014-12-01T00:00:00
    title:                        ECCO Sea Surface Height - Monthly Mean 0.5 ...
    uuid:                         08a2fc68-4158-11eb-b498-0cc47a3f6943

xarray.Dataset

• Dimensions:
  • time: 1
  • latitude: 360
  • longitude: 720
  • nv: 2
• Coordinates: (6)
  • latitude
    (latitude)
    float32
    -89.75 -89.25 ... 89.25 89.75

    axis :

    Y

    bounds :

    latitude_bnds

    comment :

    uniform grid spacing from -89.75 to 89.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    latitude at grid cell center

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)

  • latitude_bnds
    (latitude, nv)
    float32
    dask.array<chunksize=(360, 2), meta=np.ndarray>

    coverage_content_type :

    coordinate

    long_name :

    latitude bounds grid cells

    Array Chunk Bytes 2.81 kiB 2.81 kiB Shape (360, 2) (360, 2) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • longitude
    (longitude)
    float32
    -179.8 -179.2 ... 179.2 179.8

    axis :

    X

    bounds :

    longitude_bnds

    comment :

    uniform grid spacing from -179.75 to 179.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    longitude at grid cell center

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
          dtype=float32)

  • longitude_bnds
    (longitude, nv)
    float32
    dask.array<chunksize=(720, 2), meta=np.ndarray>

    coverage_content_type :

    coordinate

    long_name :

    longitude bounds grid cells

    Array Chunk Bytes 5.62 kiB 5.62 kiB Shape (720, 2) (720, 2) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • time
    (time)
    datetime64[ns]
    2014-12-16T12:00:00

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    long_name :

    center time of averaging period

    standard_name :

    time

    array(['2014-12-16T12:00:00.000000000'], dtype='datetime64[ns]')

  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    comment :

    Start and end times of averaging period.

    coverage_content_type :

    coordinate

    long_name :

    time bounds of averaging period

    Array Chunk Bytes 16 B 16 B Shape (1, 2) (1, 2) Count 2 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray

• Data variables: (3)
  • SSH
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    comment :

    Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    Dynamic sea surface height anomaly

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    valid_max :

    1.4207719564437866

    valid_min :

    -1.8805772066116333

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (1, 360, 720) (1, 360, 720) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • SSHIBC
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    comment :

    Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading

    units :

    m

    valid_max :

    0.5245633721351624

    valid_min :

    -0.30144819617271423

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (1, 360, 720) (1, 360, 720) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • SSHNOIBC
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    comment :

    Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    Sea surface height anomaly without the inverted barometer (IB) correction

    units :

    m

    valid_max :

    1.4550364017486572

    valid_min :

    -1.6654272079467773

    Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (1, 360, 720) (1, 360, 720) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

• Attributes: (57)

  Conventions :

  CF-1.8, ACDD-1.3

  acknowledgement :

  This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.

  author :

  Ian Fenty and Ou Wang

  cdm_data_type :

  Grid

  comment :

  Fields provided on a regular lat-lon grid. They have been mapped to the regular lat-lon grid from the original ECCO lat-lon-cap 90 (llc90) native model grid. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations.

  coordinates_comment :

  Note: the global 'coordinates' attribute describes auxillary coordinates.

  creator_email :

  ecco-group@mit.edu

  creator_institution :

  NASA Jet Propulsion Laboratory (JPL)

  creator_name :

  ECCO Consortium

  creator_type :

  group

  creator_url :

  https://ecco-group.org

  date_created :

  2020-12-18T09:39:51

  date_issued :

  2020-12-18T09:39:51

  date_metadata_modified :

  2021-03-15T22:07:49

  date_modified :

  2021-03-15T22:07:49

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.5

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  0.5

  geospatial_lon_units :

  degrees_east

  history :

  Inaugural release of an ECCO Central Estimate solution to PO.DAAC

  id :

  10.5067/ECG5M-SSH44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE > OCEANS > SEA SURFACE TOPOGRAPHY > SEA SURFACE HEIGHT, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Public Domain

  metadata_link :

  https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_05DEG_MONTHLY_V4R4

  naming_authority :

  gov.nasa.jpl

  platform :

  ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)

  platform_vocabulary :

  GCMD platform keywords

  processing_level :

  L4

  product_name :

  SEA_SURFACE_HEIGHT_mon_mean_2014-12_ECCO_V4r4_latlon_0p50deg.nc

  product_time_coverage_end :

  2018-01-01T00:00:00

  product_time_coverage_start :

  1992-01-01T12:00:00

  product_version :

  Version 4, Release 4

  program :

  NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)

  project :

  Estimating the Circulation and Climate of the Ocean (ECCO)

  publisher_email :

  podaac@podaac.jpl.nasa.gov

  publisher_institution :

  PO.DAAC

  publisher_name :

  Physical Oceanography Distributed Active Archive Center (PO.DAAC)

  publisher_type :

  institution

  publisher_url :

  https://podaac.jpl.nasa.gov

  references :

  ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928

  source :

  The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  summary :

  This dataset provides monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

  time_coverage_duration :

  P1M

  time_coverage_end :

  2015-01-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  2014-12-01T00:00:00

  title :

  ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4)

  uuid :

  08a2fc68-4158-11eb-b498-0cc47a3f6943

Read multiple netCDF4 files using Kerchunk reference file

Combine the individual reference files into a single time series reference object

%%time

ds_k =[]
for ref in reference_list:
    s_opts = s_opts
    r_opts = r_opts
    fs = fsspec.filesystem("reference",
                           fo=ref,
                           ref_storage_args=s_opts,
                           remote_protocol='s3',
                           remote_options=r_opts)
    m = fs.get_mapper("")
    ds_k.append(xr.open_dataset(m, engine="zarr", backend_kwargs={'consolidated':False}, chunks={}))
    
ds_multi = xr.concat(ds_k, dim='time')
    
ds_multi

CPU times: user 735 ms, sys: 31.4 ms, total: 766 ms
Wall time: 3.57 s

<xarray.Dataset>
Dimensions:         (time: 13, latitude: 360, longitude: 720, nv: 2)
Coordinates:
  * latitude        (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75
    latitude_bnds   (latitude, nv) float32 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0
  * longitude       (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8
    longitude_bnds  (longitude, nv) float32 -180.0 -179.5 -179.5 ... 179.5 180.0
  * time            (time) datetime64[ns] 2014-12-16T12:00:00 ... 2015-12-16T...
    time_bnds       (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
Dimensions without coordinates: nv
Data variables:
    SSH             (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHIBC          (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHNOIBC        (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
Attributes: (12/57)
    Conventions:                  CF-1.8, ACDD-1.3
    acknowledgement:              This research was carried out by the Jet Pr...
    author:                       Ian Fenty and Ou Wang
    cdm_data_type:                Grid
    comment:                      Fields provided on a regular lat-lon grid. ...
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            2015-01-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          2014-12-01T00:00:00
    title:                        ECCO Sea Surface Height - Monthly Mean 0.5 ...
    uuid:                         08a2fc68-4158-11eb-b498-0cc47a3f6943

xarray.Dataset

• Dimensions:
  • time: 13
  • latitude: 360
  • longitude: 720
  • nv: 2
• Coordinates: (6)
  • latitude
    (latitude)
    float32
    -89.75 -89.25 ... 89.25 89.75

    axis :

    Y

    bounds :

    latitude_bnds

    comment :

    uniform grid spacing from -89.75 to 89.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    latitude at grid cell center

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)

  • latitude_bnds
    (latitude, nv)
    float32
    -90.0 -89.5 -89.5 ... 89.5 90.0

    coverage_content_type :

    coordinate

    long_name :

    latitude bounds grid cells

    array([[-90. , -89.5],
           [-89.5, -89. ],
           [-89. , -88.5],
           [-88.5, -88. ],
           [-88. , -87.5],
           [-87.5, -87. ],
           [-87. , -86.5],
           [-86.5, -86. ],
           [-86. , -85.5],
           [-85.5, -85. ],
           [-85. , -84.5],
           [-84.5, -84. ],
           [-84. , -83.5],
           [-83.5, -83. ],
           [-83. , -82.5],
           [-82.5, -82. ],
           [-82. , -81.5],
           [-81.5, -81. ],
           [-81. , -80.5],
           [-80.5, -80. ],
    ...
           [ 80. ,  80.5],
           [ 80.5,  81. ],
           [ 81. ,  81.5],
           [ 81.5,  82. ],
           [ 82. ,  82.5],
           [ 82.5,  83. ],
           [ 83. ,  83.5],
           [ 83.5,  84. ],
           [ 84. ,  84.5],
           [ 84.5,  85. ],
           [ 85. ,  85.5],
           [ 85.5,  86. ],
           [ 86. ,  86.5],
           [ 86.5,  87. ],
           [ 87. ,  87.5],
           [ 87.5,  88. ],
           [ 88. ,  88.5],
           [ 88.5,  89. ],
           [ 89. ,  89.5],
           [ 89.5,  90. ]], dtype=float32)

  • longitude
    (longitude)
    float32
    -179.8 -179.2 ... 179.2 179.8

    axis :

    X

    bounds :

    longitude_bnds

    comment :

    uniform grid spacing from -179.75 to 179.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    longitude at grid cell center

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
          dtype=float32)

  • longitude_bnds
    (longitude, nv)
    float32
    -180.0 -179.5 ... 179.5 180.0

    coverage_content_type :

    coordinate

    long_name :

    longitude bounds grid cells

    array([[-180. , -179.5],
           [-179.5, -179. ],
           [-179. , -178.5],
           ...,
           [ 178.5,  179. ],
           [ 179. ,  179.5],
           [ 179.5,  180. ]], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2014-12-16T12:00:00 ... 2015-12-...

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    long_name :

    center time of averaging period

    standard_name :

    time

    array(['2014-12-16T12:00:00.000000000', '2015-01-16T12:00:00.000000000',
           '2015-02-15T00:00:00.000000000', '2015-03-16T12:00:00.000000000',
           '2015-04-16T00:00:00.000000000', '2015-05-16T12:00:00.000000000',
           '2015-06-16T00:00:00.000000000', '2015-07-16T12:00:00.000000000',
           '2015-08-16T12:00:00.000000000', '2015-09-16T00:00:00.000000000',
           '2015-10-16T12:00:00.000000000', '2015-11-16T00:00:00.000000000',
           '2015-12-16T12:00:00.000000000'], dtype='datetime64[ns]')

  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    comment :

    Start and end times of averaging period.

    coverage_content_type :

    coordinate

    long_name :

    time bounds of averaging period

    Array Chunk Bytes 208 B 16 B Shape (13, 2) (1, 2) Count 39 Tasks 13 Chunks Type datetime64[ns] numpy.ndarray

• Data variables: (3)
  • SSH
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    comment :

    Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    Dynamic sea surface height anomaly

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    valid_max :

    1.4207719564437866

    valid_min :

    -1.8805772066116333

    Array Chunk Bytes 12.85 MiB 0.99 MiB Shape (13, 360, 720) (1, 360, 720) Count 39 Tasks 13 Chunks Type float32 numpy.ndarray

  • SSHIBC
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    comment :

    Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading

    units :

    m

    valid_max :

    0.5245633721351624

    valid_min :

    -0.30144819617271423

    Array Chunk Bytes 12.85 MiB 0.99 MiB Shape (13, 360, 720) (1, 360, 720) Count 39 Tasks 13 Chunks Type float32 numpy.ndarray

  • SSHNOIBC
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

    comment :

    Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    Sea surface height anomaly without the inverted barometer (IB) correction

    units :

    m

    valid_max :

    1.4550364017486572

    valid_min :

    -1.6654272079467773

    Array Chunk Bytes 12.85 MiB 0.99 MiB Shape (13, 360, 720) (1, 360, 720) Count 39 Tasks 13 Chunks Type float32 numpy.ndarray

• Attributes: (57)

  Conventions :

  CF-1.8, ACDD-1.3

  acknowledgement :

  This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.

  author :

  Ian Fenty and Ou Wang

  cdm_data_type :

  Grid

  comment :

  Fields provided on a regular lat-lon grid. They have been mapped to the regular lat-lon grid from the original ECCO lat-lon-cap 90 (llc90) native model grid. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations.

  coordinates_comment :

  Note: the global 'coordinates' attribute describes auxillary coordinates.

  creator_email :

  ecco-group@mit.edu

  creator_institution :

  NASA Jet Propulsion Laboratory (JPL)

  creator_name :

  ECCO Consortium

  creator_type :

  group

  creator_url :

  https://ecco-group.org

  date_created :

  2020-12-18T09:39:51

  date_issued :

  2020-12-18T09:39:51

  date_metadata_modified :

  2021-03-15T22:07:49

  date_modified :

  2021-03-15T22:07:49

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.5

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  0.5

  geospatial_lon_units :

  degrees_east

  history :

  Inaugural release of an ECCO Central Estimate solution to PO.DAAC

  id :

  10.5067/ECG5M-SSH44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE > OCEANS > SEA SURFACE TOPOGRAPHY > SEA SURFACE HEIGHT, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Public Domain

  metadata_link :

  https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_05DEG_MONTHLY_V4R4

  naming_authority :

  gov.nasa.jpl

  platform :

  ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)

  platform_vocabulary :

  GCMD platform keywords

  processing_level :

  L4

  product_name :

  SEA_SURFACE_HEIGHT_mon_mean_2014-12_ECCO_V4r4_latlon_0p50deg.nc

  product_time_coverage_end :

  2018-01-01T00:00:00

  product_time_coverage_start :

  1992-01-01T12:00:00

  product_version :

  Version 4, Release 4

  program :

  NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)

  project :

  Estimating the Circulation and Climate of the Ocean (ECCO)

  publisher_email :

  podaac@podaac.jpl.nasa.gov

  publisher_institution :

  PO.DAAC

  publisher_name :

  Physical Oceanography Distributed Active Archive Center (PO.DAAC)

  publisher_type :

  institution

  publisher_url :

  https://podaac.jpl.nasa.gov

  references :

  ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928

  source :

  The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  summary :

  This dataset provides monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

  time_coverage_duration :

  P1M

  time_coverage_end :

  2015-01-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  2014-12-01T00:00:00

  title :

  ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4)

  uuid :

  08a2fc68-4158-11eb-b498-0cc47a3f6943

ds_multi['SSH']

<xarray.DataArray 'SSH' (time: 13, latitude: 360, longitude: 720)>
dask.array<concatenate, shape=(13, 360, 720), dtype=float32, chunksize=(1, 360, 720), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75
  * longitude  (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8
  * time       (time) datetime64[ns] 2014-12-16T12:00:00 ... 2015-12-16T12:00:00
Attributes:
    comment:                Dynamic sea surface height anomaly above the geoi...
    coverage_content_type:  modelResult
    long_name:              Dynamic sea surface height anomaly
    standard_name:          sea_surface_height_above_geoid
    units:                  m
    valid_max:              1.4207719564437866
    valid_min:              -1.8805772066116333

xarray.DataArray

'SSH'

• time: 13
• latitude: 360
• longitude: 720

• dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

  Array Chunk Bytes 12.85 MiB 0.99 MiB Shape (13, 360, 720) (1, 360, 720) Count 39 Tasks 13 Chunks Type float32 numpy.ndarray

• Coordinates: (3)
  • latitude
    (latitude)
    float32
    -89.75 -89.25 ... 89.25 89.75

    axis :

    Y

    bounds :

    latitude_bnds

    comment :

    uniform grid spacing from -89.75 to 89.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    latitude at grid cell center

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)

  • longitude
    (longitude)
    float32
    -179.8 -179.2 ... 179.2 179.8

    axis :

    X

    bounds :

    longitude_bnds

    comment :

    uniform grid spacing from -179.75 to 179.75 by 0.5

    coverage_content_type :

    coordinate

    long_name :

    longitude at grid cell center

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2014-12-16T12:00:00 ... 2015-12-...

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    long_name :

    center time of averaging period

    standard_name :

    time

    array(['2014-12-16T12:00:00.000000000', '2015-01-16T12:00:00.000000000',
           '2015-02-15T00:00:00.000000000', '2015-03-16T12:00:00.000000000',
           '2015-04-16T00:00:00.000000000', '2015-05-16T12:00:00.000000000',
           '2015-06-16T00:00:00.000000000', '2015-07-16T12:00:00.000000000',
           '2015-08-16T12:00:00.000000000', '2015-09-16T00:00:00.000000000',
           '2015-10-16T12:00:00.000000000', '2015-11-16T00:00:00.000000000',
           '2015-12-16T12:00:00.000000000'], dtype='datetime64[ns]')

• Attributes: (7)

  comment :

  Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

  coverage_content_type :

  modelResult

  long_name :

  Dynamic sea surface height anomaly

  standard_name :

  sea_surface_height_above_geoid

  units :

  m

  valid_max :

  1.4207719564437866

  valid_min :

  -1.8805772066116333

# Commenting for quarto site render
# ds_multi['SSH'].hvplot.image()

References

• https://github.com/fsspec/kerchunk
• https://medium.com/pangeo/fake-it-until-you-make-it-reading-goes-netcdf4-data-on-aws-s3-as-zarr-for-rapid-data-access-61e33f8fe685
• https://medium.com/pangeo/cloud-performant-reading-of-netcdf4-hdf5-data-using-the-zarr-library-1a95c5c92314